A Research on an Efficient Cloud Scheduling with a Geo Microarray Data Set
Selvi S1, Chandrasekar A2, Dhipa M3
1Selvi S, Department of Computer Science and Engineering, Erode Sengunthar Engineering College, Erode, Tamil Nadu, India.
2Chandrasekar A, Department of Computer Science and Engineering, Malla Reddy Institute of Technology and Science, Secunderabad, Telangana, , India.
3Dhipa M, Department of Electronics and Communication Engineering, Jairupaa College of Engineering, Tiruppur, Tamil Nadu, India.
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 2946-2952 | Volume-9 Issue-1, November 2019. | Retrieval Number: A9113119119/2019©BEIESP | DOI: 10.35940/ijitee.A9113.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Investigations on micro-array organisms for various researches have made a non discrete dealing of thousands of gene expressions achievable. For any applications, the results would be more accurate only when maximum count is analyzed within a predictable time and it is one of the unseen challenges in the field of bio medicine. The purpose of this data analysis is to regulate and control the activities of thousands of genes in our body. This paper develops a scheduling analysis of how effectively gene molecular patterns are taken into experimentation. This motivated our investigation in a new dimension for a cloud environment. This paper is about applying our previous works such as Workflow Shuffling and Hole Filling Algorithm (WSHF) [13], Agent Centric Enhanced Reinforcement learning algorithm (AGERL) [14], Heuristic Flow Equilibrium based Load Balancing (HFEL) [15] and Dynamic Resource Provisioning and Load Balancing (DRBLHS) [16] algorithms collaboratively for a Gene Express Omnibus dataset as a case study. The gene data’s plays an important role in monitoring the human activities and how well, the data has been processed in the cloud with minimum budget, time and minimum virtual machines. Finally, the efficiency of the system is analyzed in terms of resource utilization, completion time, response time, throughput and VM Migration time.
Keywords: Cloud Provider, Execution Process, WSHF, AGERL, HFEL, DRBLHS, Gene Expression Omnibus Dataset, Resource Utilization, Workflow Completion Time, Response Time, Throughput and VM Migration Time
Scope of the Article: Cloud Resources Utilization in IoT